Method for finding objects

ABSTRACT

What is disclosed is a computer-implemented method for finding objects which are based on data structures, the method comprising the following steps: announcing a selection of objects; generating and/or invalidating a characterization of one object or several objects of the selection, with characterization being effected by means of an item of characterization information; calculating weights of the objects of the selection and/or of objects located in a vicinity of the characterized objects based on the characterization information, weights already calculated, and/or weights already stored; and modifying the selection and continuing the method from the first step until objects to be found are contained in the selection, until the method is interrupted, or until a given or calculated number of method steps has been carried out, with the possibility of terminating or interrupting the method after any one of the preceding steps.

The present invention relates to a computer-implemented method forfinding objects, and in particular a method wherein a selection issupported by indications obtained from items of information which areintroduced into the method by suitably generating or invalidating acharacterization of objects already selected.

In the prior art, several searching methods are known which are used forlocating particular objects.

On the one hand, there is a searching method which performs a searchwith the aid of entries and ranges of attribute values which may belinked through logical terms. One example for such a query is theexpression “Query((hobby IS photography) AND (age<40))”. As a result ofsuch a query, all those objects are returned to which the condition inthe query applies. Such a search is not applicable for merely vaguequantities and frequently fails owing to orthographic errors,ambiguities and synonyms in search terms, such as“fotografieren=photographieren [taking photographs]=knipsen [shootingphotographs]”.

There furthermore are searching methods making use of a hierarchicalThesaurus, which is comparable to leafing through an index list of abook. The drawback of such a searching method is that apart from arigidly predetermined index structure, a user is not guided towards atarget. In addition, the user cannot interactively control or influencethe searching method. One example for such a searching method is thefile explorer in the “Windows” operating system by the companyMicrosoft.

Finally, there are evolutionary searching methods where the user isoffered a great many objects. The user then evaluates one or severalones of the objects in accordance with similarity with a sought object,and with the aid of this information a new selection of objects isdetermined by using a genetic or evolutionary algorithm, which selectionis then announced to the user. The process is terminated when the userrecognizes the object sought by him among the offered number of objects.It is one drawback of this searching method that the selection of anamount of information to be offered is performed solely on the basis ofsimilarities between objects. The searching method is performed in aprobabilistic manner. This means that the user will find a sought objectwith a particular probability only. In addition it is not clear when asearch is completed.

The present invention was achieved in order to remedy the abovementioned problems in the prior art.

Consequently, it is the object of the present invention to furnish amethod for finding objects, whereby objects may be found reliably andsimply.

This object is attained through the measures specified in claim 1.

Further advantageous embodiments of the present invention are subjectmatter of the dependent claims.

More specifically, a computer-implemented method in accordance with theinvention for finding objects which are based on data structures,comprises the following steps: (a) announcing a selection of objects;(b) generating and/or invalidating a characterization of one object orseveral objects of the selection, wherein characterization is effectedby means of characterization information; (c) calculating weights of theobjects of the selection and/or of objects in a vicinity of thecharacterized objects based on the characterization information, weightsalready calculated, and/or weights already stored; and (d) modifying theselection and continuing the method from step (a) until objects to befound are contained in the selection, until the method is interrupted,or until a given or calculated number of method steps has been carriedout, wherein the method may be terminated or interrupted after any oneof steps (a) to (d).

In accordance with the invention one obtains the effect that throughgenerating and/or invalidating a characterization of one or severalobjects and selecting the objects, only particular objects and not allobjects have to be taken into consideration in subsequent method steps,whereby an efficient memory utilization becomes possible which cannot beachieved with previous methods.

In addition, due to iterative repetition of the method steps, generatingand/or invalidating a characterization and/or selection of the objectsmay be carried out in adaptation to a respective query and a searchresult already obtained, so that objects to be found may be foundreliably and simply, whereby the drawbacks of the prior art are avoided.

In accordance with a development of the present invention, weights ofthe objects of the selection are also announced.

In accordance with another development of the present invention, theobjects include texts, numerals, geometrical shapes, graphicrepresentations, picture documents, video documents, audio documents, orparts or combinations of these.

In accordance with another development of the present invention, theweights are represented through a vector having a real-number value inan n-dimensional space.

In accordance with another development of the present invention, upongenerating a characterization of an object, this object is associatedwith a point in an n-dimensional space, and upon invalidating acharacterization of an object, the association of the object with thispoint is invalidated again.

In accordance with another development of the present invention, theweights of an object are labelled with the aid of color, texture, shape,acoustic signals, animation, graphical characterization, textualcharacterization, arrangement on a graphic user surface, or combinationsof these.

In accordance with another development of the present invention, theweights of an object are determined through measurement of the distancesfrom the characterized objects, through the properties of the latter,through the properties of objects on a respective path to thecharacterized objects, or through combinations of these.

In accordance with another development of the present invention, aprescription for a calculation of weights of an object depends on thisobject or on an object in a vicinity thereof.

In accordance with another development of the present invention, theobjects are integrated into a hierarchical network of objects.

In accordance with another development of the present invention, in step(a) and/or step (d) the selection takes into consideration objectshaving a higher position in the hierarchy as well as a vicinity ofthese.

In accordance with another development of the present invention, in step(a) and/or step (d) the selection takes into consideration those objectsand a vicinity thereof which result from a textually or graphicallyformulated query.

In accordance with another development of the present invention, in step(a) and/or (d) the selection takes into consideration those objects anda vicinity thereof which possess particular weights and/or propertiesbased on stored information.

In accordance with another development of the present invention, thecalculated weights and/or the characterized objects of a user or of aselection of users are stored, and this information is used for settingthe weights at the outset of the method and/or for modifying the weightscalculated in the course of the method.

In accordance with another development of the present invention, thecalculated weights in the objects, through a measure of relevance,provide information on the extent to which a respective object pertainsto at least one characterized object.

In accordance with another development of the present invention, thecalculated weights in the objects, through a measure of relevance,provide information on the extent to which a respective object pertainsto all characterized objects.

In accordance with another development of the present invention,modifying the selection in step (d) is performed with the aid of acorresponding hardware device.

In accordance with another development of the present invention,modifying the selection in step (d) is performed automatically with theaid of the calculated weights.

In accordance with another development of the present invention, in step(d) the selection takes into consideration those objects including amaximum relevance for a search and/or objects in a vicinity thereof,with a measure of relevance being calculated as a special form of aweight based on the other weights.

In accordance with another development of the present invention, in step(d) the selection corresponds to navigating in a quantity of allobjects, with a new selection and an already existing selection jointlycomprising one or several objects in a navigation.

In accordance with another development of the present invention, theobjects are integrated into an n^(th)-order fractal-hierarchical networkof objects, and properties of the links between objects influence thecalculation of the weights in step (c) and/or the selection of theobjects in step (a) and/or step (d).

The present invention shall hereinbelow be explained more closely by wayof embodiments while making reference to the accompanying drawings,wherein:

FIG. 1 is a schematic view of a first method step in a method inaccordance with an embodiment of the present invention;

FIG. 2 is a schematic view of a second method step in the method inaccordance with the embodiment of the present invention;

FIG. 3 is a schematic view of a third method step in the method inaccordance with the embodiment of the present invention;

FIG. 4 is a schematic view of a fourth method step in the method inaccordance with the embodiment of the present invention;

FIG. 5 is a schematic view of a fifth method step in the method inaccordance with the embodiment of the present invention;

FIG. 6 is a schematic view of a sixth method step in the method inaccordance with the embodiment of the present invention; and

FIG. 7 is a schematic view of a seventh method step in the method inaccordance with the embodiment of the present invention;

Before giving a more detailed explanation of an embodiment of thepresent invention, it is noted that the present invention mayadvantageously be applied to a semantic or fractal network as described,for example, in the application by the applicant of the presentinvention bearing serial No. [DE] 199 08 204.9 and entitled “FraktalesNetz n-ter Ordnung zum Behandeln komplexer Strukturen” [n^(th)-Orderfractal network for handling complex structures], filed on Feb. 25,1999, and the application by the applicant of the present inventionbearing serial No. 199 17 592.6 and entitled “Situationsabhängigoperierendes semantisches Netz n-ter Ordnung” [n^(th)-Order semanticnetwork allowing for situation-dependent operation], filed on Apr. 19,1999.

It is noted, however, that the present invention is not restricted touse with a like network of objects. Rather, the present invention may beapplied wherever objects exist which are based on data structures, andwhere a measure of a distance between objects can be defined.

The following is a description of an embodiment of the presentinvention.

FIGS. 1 to 7 show schematic views of first to seventh method steps in amethod in accordance with the embodiment of the present invention.

FIG. 1 is a representation of a network of objects including objects 1through 12. To each one of these objects, an n-dimensional vector ofnumbers may be associated which represents weights in the searchingmethod to be performed. As can be seen in FIG. 1, no weights have beenallotted at the beginning of the searching method in this embodiment ofthe present invention. In a frame drawn as a dashed line in FIG. 1, aselection is entered which is selected at the outset of the searchingmethod. Thus a selection of objects is announced in a first step of thesearching method. In this embodiment of the present invention, object 9is to be searched which, in terms of a query, is situated in theneighborhood or vicinity of objects 7 and 11, as is represented by thelines connecting objects 7 and 9 and objects 9 and 11 in FIG. 1. Object9, on the other hand, is not located in the vicinity of object 10.

As is shown in FIG. 2, object 1 is now labelled by a user by generatinga characterization and provided with characterization informationMa=0.0, as is shown in the triangular box of object 1 in FIG. 2. Inother words, a second step is performed in which a characterization ofan object or of a number of objects of the selection may be generated orinvalidated. Characterization in this step is performed by means of anitem of characterization information such as, for example, Ma, which isassigned a particular value indicating the degree of relevance an objectcharacterized by this value has for a respective search.

As is shown in FIG. 2 in quadrangular boxes of objects 1, 2, 3, 5, 6 and7, weights of the objects 1, 2, 3, 5, 6 and 7 are calculated for allobjects 1, 2, 3, 5, 6 and 7 of the selection in a third step, such thata weight WKx of an object K behaves as a function of a distance DKL froma characterized object L having a characterization information Mx, as isexpressed by the following equation (1): $\begin{matrix}{{WKx} = {{{Mx} - {\mathbb{e}}^{{- {pa}}*{DKL}^{pb}}}}} & (1)\end{matrix}$

with: pa=0.25 and pb=2.0

This equation (1) represents a Gaussian distribution. It is, of course,also possible to use any other form and/or parametrization of thedependency. In this embodiment of the present invention, values for WKaof 1.0, 0.37, 0.0, 0.78, 0.11 and 0.0, respectively result from theabove equation (1) for the objects 1, 2, 3, 5, 6 and 7 contained in theselection. The higher the value of a weight WKx, the more relevant anobject having this weight is with regard to the item of characterizationinformation Mx for a respective search.

As is shown in FIG. 3, for an identical selection the object 7 is now,in the same manner as described before with regard to object 1,characterized by a characterization information Mb=0.0, and weights WKbof all objects 1, 2, 3, 5, 6 and 7 present in the selection arecalculated anew. In this embodiment of the present invention, weightsWKb of 0.0, 0.02, 0.78, 0.0, 0.11 and 1.0, respectively, result for theobjects 1, 2, 3, 5, 6 and 7 contained in the selection. If objects areto be found which best match all characterized objects, then a measureof relevance may be defined as a special weight. This measure ofrelevance indicates information concerning a progress of search. Thegreater the value of a measure of relevance, the closer a respectiveobject is situated to a searched object. A possible form of thedefinition of the measure of relevance of an object K is given in thefollowing equation (2):RK=min(S−WKa, S−WKb, . . . )   (2)

with S=sum(WKa, WKb, . . . ),

wherein the minimization min and the summation sum covers all weightsWKx of an object.

The measures of relevance calculated in this embodiment of the presentinvention with the aid of equation (2) for the objects 1, 2, 3, 5, 6 and7 that are contained in the selection are indicated in the oval boxes ofthe respective objects 1, 2, 3, 5, 6 and 7 in FIG. 3. It is, however,also possible to use other dependencies in deriving a measure ofrelevance, which is possible in particular when weights alreadycalculated in the past play a role in the searching method. As can beseen from FIG. 3, there results in this embodiment of the presentinvention a maximum measure of relevance R6=0.11 for object 6. If, now,as a new selection the vicinity around object 6 is selected, thereresults the selection which is shown in FIG. 4 and contains objects 2,6, 10 and 11.

The expression “vicinity” as used here is closely linked with theexpression “distance”. All those objects having a measure of distancefrom a given object that is smaller than a predefined or calculatedthreshold, are considered to be neighbors of that given object. Adetermination of the measure of distance depends on the respectivemethod used, which either is the same in the entire quantity of objects,or depends on single objects, or is the same in respective partialquantities of the quantity of objects and/or dependent on properties ofthe objects and/or on properties of relations between objects. In thisembodiment of the present invention, the measure of distance between twoobjects is calculated from the minimum number of links having to bepassed through in order to get from one object to another one. Thevicinity around a given object is in this embodiment of the presentinvention defined as a quantity of objects having a measure of distancefrom a given object that is smaller than two. From this, there resultsfor object 6 in FIG. 4 a vicinity of the quantity of objects 2, 10, 11as the new selection as represented by the dashed frame in FIG. 4.

In accordance with the representation in FIG. 5, all weights WKa and WKbof the objects 2, 6, 10 and 11 present in the new selection and notalready calculated are calculated anew, and all weights WKa and WKbalready calculated of the objects 1, 3, 5 and 7 not contained any morein the new selection are discarded. If rapid calculation is to beensured in view of constant modification of the selection, such as whennavigating through the network of objects, it may be expedient to storeall weights already calculated.

If now, for example, object 10 is considered to be entirely irrelevantfor a respective search, then object 10 may be characterized through anitem of characterization information of Mc=1.0 in accordance with therepresentation of FIG. 6. Weights WKa, WKb and WKc and measures ofrelevance R2, R6, R10 and R11 of objects 2, 6, 10 and 11 accordingly arecalculated anew, as is shown in FIG. 6. As may be taken from FIG. 6, asa result object 11 having a maximum measure of relevance of R11=0.39turns out to be the most relevant object of the present selectionincluding objects 2, 6, 10 and 11.

As is shown in FIG. 7, the selection is now modified such that object 11having the maximum measure of relevance R11=0.39 and its neighboringobjects 6, 8 and 9 are contained in the new selection. Followingrecalculation of the weights WKa, WKb and WKc and of the measures ofrelevance R6, R8, R9 and R11, object 9 is allocated measure of relevanceR9=0.78 in this embodiment of the present invention. Because object 9 isthe sought object, the sought object 9 was found in this embodiment ofthe present invention, and the method may be terminated.

The above described method may advantageously be applied in any cases inwhich merely a vague formulation may be given for a query and/or asearch for an object requires information from the vicinity of thisobject or a context, respectively.

Concrete applications are any forms of database inquiries of complexlystructured information such as, e.g., contents of text documents, audiodocuments or picture documents. Besides the text documents, audiodocuments or picture documents, the objects may also be texts, numerals,geometrical shapes, graphic representations or parts or combinations ofall the above mentioned kinds.

In addition, by means of the above described method it is also possibleto realize interactive “guidance” of customers towards products whichthe customers wish to purchase in e-commerce solutions, or thepurposeful search for replies to customer's inquiries inside a givennetwork of objects in help-desk applications.

Thanks to the above described method there moreover exists thepossibility of replying to any formulated inquiries with expert systems.This is particularly possible in expert systems for problem diagnosis inmedicine and technology. In a medical expert system, for example,patient data and symptoms associated with respective results may bedepicted as an object in a network of objects. Through the abovedescribed method, it is very easy in a like network of objects torestrict the number of possible results by iterative characterizationand new selection of objects.

Another application exists in the area of risk management andsensitivity analysis. In this case, it is possible to characterizeobjects representing risks. Based on this characterization and the abovedescribed method steps, it is then possible to obtain a selection of allthose objects that are dependent on these risks.

In an analogously inverted method, all those objects are characterizedthe sensitivity of which is to be inspected with regard to risks. Theabove described method then iteratively finds a selection of risksacting on respective characterized objects.

The above described embodiment of the present invention merelyconstitutes one possible development of the method of the invention.

The present invention is, however, not restricted to this embodiment.Further possible developments of the method of the invention aredescribed hereinbelow.

Although weights of objects of the selection are calculated in the abovedescribed embodiment of the present invention, there moreover is alsothe possibility of additionally or instead calculating weights ofobjects in the vicinity of the characterized objects based on the itemof characterization information, weights already calculated, and/orweights already stored. The method may be continued until objects to befound are contained in the selection, the method is interrupted, or agiven or calculated number of method steps has been performed. Themethod may herein be terminated or interrupted after any single methodstep.

In the above described embodiment, at the start of the method no weightsare announced for the objects contained in a selection when theselection is announced. When announcing the selection it is, however,also possible to jointly announce weights of the objects contained inthe selection.

The weights of the objects may, for example, be represented through areal-value vector in an n-dimensional space. Moreover the generation ofa characterization of an object may be performed such that this objectis associated with a point in an n-dimensional space. If thecharacterization is invalidated again, the association of this objectwith the point is invalidated again.

In general, the weights of the object may be labelled by means of color,texture, shape, acoustic signals, animation, graphical characterization,textual characterization, arrangement on a graphic user surface, orcombinations of these.

Weights of the objects may, for example, be determined throughmeasurement of the distances from characterized objects, through theproperties of the latter, through the properties of objects on arespective path to the characterized objects, or through combinations ofthese. There moreover is the possibility of calculating the weights ofan object depending on this object or on an object in a vicinity of thelatter.

The method according to the invention may advantageously be applied toobjects integrated into a hierarchical network of objects, with theequally existing possibility of the selection taking into considerationobjects having a higher position in the hierarchy and a vicinity ofthese.

In the method of the invention, a query may, for example, be formulatedtextually or graphically. In such case, a respective selection takesinto consideration those objects, as well as a vicinity thereof, whichensue as a result based on the query. There moreover exists thepossibility of a respective selection taking into consideration thoseobjects and a vicinity thereof, which possess particular weights and/orproperties due to stored information.

Another advantageous development of the present invention consists instoring calculated weights and/or characterized objects of a user or ofa selection of users and utilizing this information, in order to setweights at the start of the method and/or modify weights calculated inthe course of the method.

Generally speaking, a user may be understood to be either a person, agroup of persons, a software program or a suitable hardware device,wherein, for example, generating and/or invalidating a characterizationand/or varying the selection may be carried out with the aid of acorresponding hardware device. There also is the possibility, however,of a selection being modified automatically by using calculated weights.

Calculated weights in the objects may, by way of the measure ofrelevance, provide information either about the degree of pertinence ofa respective object to at least one characterized object, or informationabout the degree of relevance of a respective object for allcharacterized objects.

The selection may correspond to navigation in a quantity of all objects.When navigation is performed, a new selection and an already existingselection may have one or several objects in common.

The objects may finally be integrated into an n^(th)-orderfractal-hierarchical network, and properties of links between objectsmay influence the calculation of weights and the selection of objects.

It accordingly is obvious that the present invention may be implementedin most variegated manners, so that the present invention should not beconsidered to be restricted to the above described embodiment.

1. Computer-implemented method for finding objects which are based ondata structures, said method comprising the following steps: (a)announcing a selection of objects; (b) generating and/or invalidating acharacterization of one object or several objects of said selection,wherein characterization is effected by means of characterizationinformation; (c) calculating weights of the objects of said selectionand/or of objects in a vicinity of said characterized objects based onsaid characterization information, weights already calculated, and/orweights already stored; and (d) modifying said selection and continuingthe method from step (a) until objects to be found are contained in saidselection, until the method is interrupted, or until a given orcalculated number of method steps has been carried out, wherein themethod may be terminated or interrupted after any one of steps (a) to(d).
 2. The method of claim 1, wherein in step (a) weights of theobjects of said selection may also be announced.
 3. The method of one ofthe preceding claims, wherein said objects include texts, numerals,geometrical shapes, graphic representations, picture documents, videodocuments, audio documents or parts or combinations of these.
 4. Themethod of one of the preceding claims, wherein said weights arerepresented through a vector having a real-number value in ann-dimensional space.
 5. The method of one of the preceding claims,wherein upon generating a characterization of an object, this object isassociated with a point in an n-dimensional space, and upon invalidatinga characterization of an object, the association of said object withthis point is invalidated again.
 6. The method of one of the precedingclaims, wherein the weights of an object are labelled with the aid ofcolor, texture, shape, acoustic signals, animation, graphicalcharacterization, textual characterization, arrangement on a graphicuser surface, or combinations of these.
 7. The method of one of thepreceding claims, wherein the weights of an object are determinedthrough measurement of the distances from said characterized objects,through the properties of the latter, through the properties of objectson a respective path to said characterized objects, or throughcombinations of these.
 8. The method of one of the preceding claims,wherein a prescription for a calculation of the weights of an objectdepends on this object or on an object in a vicinity thereof.
 9. Themethod of one of the preceding claims, wherein said objects areintegrated into a hierarchical network of objects.
 10. The method ofclaim 9, wherein in step (a) and/or step (d) the selection takes intoconsideration objects having a higher position in the hierarchy as wellas a vicinity of these.
 11. The method of one of the preceding claims,wherein in step (a) and/or step (d) the selection takes intoconsideration those objects and a vicinity thereof which result from atextually or graphically formulated query.
 12. The method of one ofclaims 1 to 10, wherein in step (a) and/or (d) the selection takes intoconsideration those objects and a vicinity thereof which possessparticular weights and/or properties based on stored information. 13.The method of one of the preceding claims, wherein the calculatedweights and/or the characterized objects of a user or of a selection ofusers are stored, and this information is used for setting the weightsat the outset of the method and/or for modifying the weights calculatedin the course of the method.
 14. The method of one of the precedingclaims, wherein through a measure of relevance the calculated weights insaid objects provide information on the extent to which a respectiveobject pertains to at least one characterized object.
 15. The method ofone of claims 1 to 13, wherein through a measure of relevance thecalculated weights in said objects provide information on the extent towhich a respective object pertains to all characterized objects.
 16. Themethod of one of the preceding claims, wherein modifying said selectionin step (d) is performed with the aid of a corresponding hardwaredevice.
 17. The method of one of claims 1 to 15, wherein modifying saidselection in step (d) is performed automatically with the aid of saidcalculated weights.
 18. The method of claim 17, wherein in step (d) saidselection takes into consideration those objects including a maximumrelevance for a search, and/or objects in a vicinity thereof, with ameasure of relevance being calculated as a special form of a weightbased on the other weights.
 19. The method of one of the precedingclaims, wherein in step (d) said selection corresponds to navigating ina quantity of all objects, with a new selection and an already existingselection jointly comprising one or several objects in a navigation. 20.The method of claim 1, wherein said objects are integrated into ann^(th)-order fractal-hierarchical network of objects, and properties ofthe links between objects influence calculation of the weights in step(c) and/or selection of objects in step (a) and/or step (d).